Search Results for author:
Found 4 papers, 1 papers with code
Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation
Vision transformers (ViTs) achieve remarkable performance on large datasets, but tend to perform worse than convolutional neural networks (CNNs) when trained from scratch on smaller datasets, possibly due to a lack of local inductive bias in the architecture.
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation
Instead of employing thresholding on predictions, we introduce a method to weight the gradients calculated from pseudo-labels based on the reliability of the teacher's predictions.
Self-training Guided Adversarial Domain Adaptation For Thermal Imagery
To this end, we propose a self-training guided adversarial domain adaptation method to promote generalization capabilities of adversarial domain adaptation methods.
GAIT: Gradient Adjusted Unsupervised Image-to-Image Translation
In this work, we propose an unsupervised IIT method that preserves the uniform regions after the translation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
